本文整理汇总了Python中tensorflow.contrib.distribute.python.values.select_device_mirrored函数的典型用法代码示例。如果您正苦于以下问题:Python select_device_mirrored函数的具体用法?Python select_device_mirrored怎么用?Python select_device_mirrored使用的例子?那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了select_device_mirrored函数的7个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: testWrapClass
def testWrapClass(self):
# Normally a mirrored value would be the same across devices, but
# for a test it is convenient to be able to tell the values apart.
result = values.regroup({_device_str(0): _nested_value("1"),
_device_str(1): _nested_value("2")},
values.Mirrored)
self.assertIsInstance(result, tuple)
self.assertEqual(3, len(result))
self._is_per_device(result[0], ["a1", "a2"], values.Mirrored)
self._is_per_device(result[2], ["h1", "h2"], values.Mirrored)
self.assertIsInstance(result[1], list)
self.assertEqual(3, len(result[1]))
self._is_per_device(result[1][0], ["b1", "b2"], values.Mirrored)
self._is_per_device(result[1][2], ["g1", "g2"], values.Mirrored)
self.assertIsInstance(result[1][1], dict)
self.assertEqual(set(["c", "e"]), set(result[1][1].keys()))
self._is_per_device(result[1][1]["c"], ["d1", "d2"], values.Mirrored)
self._is_per_device(result[1][1]["e"], ["f1", "f2"], values.Mirrored)
# Also test that we can undo the merge using select_device()
self.assertEqual(_nested_value("1"),
values.select_device(_device_str(0), result))
self.assertEqual(_nested_value("2"),
values.select_device(_device_str(1), result))
# Values are marked as mirrored, so select_device_mirrored() is allowed.
self.assertEqual(_nested_value("1"),
values.select_device_mirrored(_device_str(0), result))
self.assertEqual(_nested_value("2"),
values.select_device_mirrored(_device_str(1), result))
开发者ID:Huoxubeiyin,项目名称:tensorflow,代码行数:31,代码来源:values_test.py
示例2: testNested
def testNested(self):
result = values.regroup({_device_str(0): _nested_value("1"),
_device_str(1): _nested_value("2")})
self.assertIsInstance(result, tuple)
self.assertEqual(3, len(result))
self._is_per_device(result[0], ["a1", "a2"])
self._is_per_device(result[2], ["h1", "h2"])
self.assertIsInstance(result[1], list)
self.assertEqual(3, len(result[1]))
self._is_per_device(result[1][0], ["b1", "b2"])
self._is_per_device(result[1][2], ["g1", "g2"])
self.assertIsInstance(result[1][1], dict)
self.assertEqual(set(["c", "e"]), set(result[1][1].keys()))
self._is_per_device(result[1][1]["c"], ["d1", "d2"])
self._is_per_device(result[1][1]["e"], ["f1", "f2"])
# Also test that we can undo the merge using select_device()
self.assertEqual(_nested_value("1"),
values.select_device(_device_str(0), result))
self.assertEqual(_nested_value("2"),
values.select_device(_device_str(1), result))
# select_device_mirrored() should fail due to non-mirrored values
with self.assertRaises(TypeError):
values.select_device_mirrored(_device_str(0), result)
with self.assertRaises(TypeError):
values.select_device_mirrored(_device_str(1), result)
开发者ID:Huoxubeiyin,项目名称:tensorflow,代码行数:28,代码来源:values_test.py
示例3: _update_non_slot
def _update_non_slot(self, colocate_with, fn, *args, **kwargs):
assert isinstance(colocate_with, list)
# TODO(josh11b): In eager mode, use one thread per device.
updates = {}
for d in colocate_with:
name = "update_%d" % self._device_index.get(d)
with ops.device(d), distribute_lib.UpdateContext(d), ops.name_scope(name):
updates[d] = fn(*values.select_device_mirrored(d, args),
**values.select_device_mirrored(d, kwargs))
return values.regroup(updates, values.Mirrored)
开发者ID:Jordan1237,项目名称:tensorflow,代码行数:10,代码来源:mirrored_strategy.py
示例4: _update
def _update(self, var, fn, *args, **kwargs):
# TODO(josh11b): In eager mode, use one thread per device.
assert isinstance(var, values.DistributedVariable)
updates = {}
for d, v in var._index.items(): # pylint: disable=protected-access
name = "update_%d" % self._device_index.get(d)
with ops.device(d), distribute_lib.UpdateContext(d), ops.name_scope(name):
# If args and kwargs are not mirrored, the value is returned as is.
updates[d] = fn(v,
*values.select_device_mirrored(d, args),
**values.select_device_mirrored(d, kwargs))
return values.regroup(updates, values.Mirrored)
开发者ID:Jordan1237,项目名称:tensorflow,代码行数:12,代码来源:mirrored_strategy.py
示例5: _update
def _update(self, var, fn, *args, **kwargs):
# TODO(josh11b): Also support TowerLocalVariables here? If so, args and
# kwargs don't need to be mirrored.
assert isinstance(var, values.MirroredVariable)
# TODO(josh11b): In eager mode, use one thread per device.
updates = {}
for d, v in var._index.items(): # pylint: disable=protected-access
name = "update_%d" % self._device_index.get(d)
with ops.device(d), distribute_lib.UpdateContext(d), ops.name_scope(name):
updates[d] = fn(v,
*values.select_device_mirrored(d, args),
**values.select_device_mirrored(d, kwargs))
return values.regroup(updates, values.Mirrored)
开发者ID:jfreedman0,项目名称:tensorflow,代码行数:13,代码来源:mirrored_strategy.py
示例6: map
def map(self, map_over, fn, *args, **kwargs):
# TODO(josh11b): In eager mode, use one thread per device.
index = {}
for i, m in enumerate(map_over):
d = self._devices[i % len(self._devices)]
with ops.device(d):
l = index.get(d, [])
l.append(fn(m,
*values.select_device_mirrored(d, args),
**values.select_device_mirrored(d, kwargs)))
index[d] = l
# TODO(josh11b): Need a values.regroup equivalent that handles MapOutput
# in addition to PerDevice data.
return values.PerDevice({k: values.MapOutput(v) for k, v in index.items()})
开发者ID:Jordan1237,项目名称:tensorflow,代码行数:14,代码来源:mirrored_strategy.py
示例7: _update
def _update(self, var, fn, *args, **kwargs):
# TODO(jhseu): Consider supporting grouped==False.
assert isinstance(var, values.TPUMirroredVariable)
if values._enclosing_tpu_context() is not None: # pylint: disable=protected-access
return fn(var, *args, **kwargs)
# Otherwise, we revert to MirroredStrategy behavior and update each variable
# directly.
updates = {}
for d, v in var._index.items(): # pylint: disable=protected-access
name = "update_%d" % self._device_index.get(d)
with ops.device(d), distribute_lib.UpdateContext(d), ops.name_scope(name):
# If args and kwargs are not mirrored, the value is returned as is.
updates[d] = fn(v,
*values.select_device_mirrored(d, args),
**values.select_device_mirrored(d, kwargs))
# Make a single control dependency to keep the variables mirrored. If one
# assignment is fetched, then run all assignments.
sorted_keys = sorted(updates.keys())
update_tuple = control_flow_ops.tuple([updates[d] for d in sorted_keys])
for i, d in enumerate(sorted_keys):
updates[d] = update_tuple[i]
return values.regroup(updates, values.Mirrored)
开发者ID:Jordan1237,项目名称:tensorflow,代码行数:24,代码来源:tpu_strategy.py
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